(Master) Data Management and Data Governance: Dream Team!

It is an open secret that Master Data Management and Data Governance are both initiatives being successful only as a double package

I believe there are numerous articles and blog posts in the web, stating and proving exactly the fact: (Master) Data Management without Data Governance is a very high risk for money burning. But why?

Conflict within Data Definitions

A simple example from true life and countless data migration and data integration projects helps. In general, all data sources in a company have their own data schema and each schema has its own individual data fields with individual data field names and individual field content, according to the interpretation of the field names and processes. In some systems, the name elements are split up into various fields like name 1, name 2, name 3 and name 4. In another system, there are two fields: first name and last name. The next system offers a name line, containing all name elements including the title. Of course each of the system is used in operational business and perfectly covering the requirements of the follow-up processes. No doubt about that.

The first potential conflict at this point is that there are many data and system owners, each of them considering their own data as the most relevant and the most correct one. From their individual point of view, a fair approach. How to solve this conflict?

In general, the discussion it is not only about the best data source; it is also about data definitions and standards. The question „how is the term customer defined“ is the most popular one, because there are several definitions in one enterprise and each of them is probably correct, always depending on the context and the point of view. That is the second potential conflict: what are the definitions of data fields and does the data meet the given definitions?

If a company decides on master data management to get advantage from shared data and harmonized business processes to achieve operational excellence, then processes and standards have to be defined. It is of fundamental importance, that there is only one definition for each business term, that all stakeholders agree on and that each employee is aware of (or at least has access to the data dictionary and / or business glossary). The agreement on standards and definitions, the publication of them and making them present is the objective of data governance. Once there is an agreement on how to manage data, on the data schema and on the processes dealing with the data, then master data management projects can be realized more easily and efficiently.

Don't forget the metrics

There is one additional important point: once standards and definitions are defined, there should be metrics to make sure that the desired data quality is maintained. Because only if the (source) data are of good quality, all following processes are effective and at least able to support the increase of revenue. The well-known term “garbage in – garbage out” describes exactly this phenomenon.

The art of metrics is to find the point of break-even. When exactly is the moment to start actions to re-improve or optimize data to avoid process failure? And if there are indicators of potential weak data quality, where, at which system, which data field and which process is it worth to invest? These questions are even more important when dealing with several legal systems feeding into a central MDM system (or during an implementation project of a new CRM system, for example). The aim of measuring is to achieve the highest degree of transparency on data quality of master data to ensure good results of postponed processes.

In summary there are three challenges described: firstly, the agreement on how to deal with the data coming from different source systems. Secondly, to fill the gap on agreement on definitions, standards and processes. And last but not least, the definition of metrics on data. Good news: at least for measuring data quality there is a smart solution: we offer a tool for measuring data quality based on individual data quality rules. Each record can be checked against several rules and the rule results are aggregated to a single data quality key performance indicator. With drill-down the root causes of a low KPI can be easily identified and actions of data quality improvement can be initiated. Of course, benchmark functionality is included.

Certainly, this approach does not cover all items of a complete data governance framework. However, it is a good starting point for fact-based discussions on data quality and effective data management initiatives. This is why (Master) data management and data governance are a dream team.